{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T07:21:21Z","timestamp":1787901681807,"version":"build-2784847793"},"reference-count":43,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Wound Journal"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like \u2018machine learning\u2019, \u2018surgical\u2019 and \u2018wound\u2019. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn\u2010grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.<\/jats:p>","DOI":"10.1111\/iwj.14665","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T23:08:55Z","timestamp":1706051335000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["RETRACTED: Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review"],"prefix":"10.1111","volume":"21","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[{"name":"The Second Clinical Medical School Lanzhou University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junde","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine, Health Science Center Lanzhou University  Lanzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7347-6574","authenticated-orcid":false,"given":"Ramyar","family":"Farzan","sequence":"additional","affiliation":[{"name":"Department of Plastic &amp; Reconstructive Surgery, School of Medicine Guilan University of Medical Sciences  Rasht Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8535-3881","authenticated-orcid":false,"given":"Hamidreza","family":"Alizadeh Otaghvar","sequence":"additional","affiliation":[{"name":"Associate Professor of Plastic Surgery, Trauma and Injury Research Center Iran University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"issue":"2","key":"e_1_2_8_2_1","first-page":"295","article-title":"Surgical wound infections","volume":"6","author":"Barry CL","year":"2021","journal-title":"Phys Assist Clinic"},{"key":"e_1_2_8_3_1","unstructured":"Sandy\u2010HodgettsK OuseyK ConwayB et al.International Best Practice Recommendations for the Early Identification and Prevention of Surgical Wound Complications.2020."},{"issue":"6","key":"e_1_2_8_4_1","first-page":"481","article-title":"Factors influencing antibiotic prophylaxis for surgical site infection prevention in general surgery: a review of the literature","volume":"52","author":"Gagliardi AR","year":"2009","journal-title":"Can J Surg"},{"key":"e_1_2_8_5_1","volume-title":"Global Guidelines for the Prevention of Surgical Site Infection","author":"Organization WH","year":"2016"},{"key":"e_1_2_8_6_1","article-title":"Debridement for surgical wounds","volume":"9","author":"Smith F","year":"2013","journal-title":"Cochrane Database Syst Rev"},{"issue":"1","key":"e_1_2_8_7_1","first-page":"1","article-title":"Nursing the future: how artificial intelligence empowers critical care nurses to revolutionize intensive care unit rehabilitation","volume":"2","author":"Emami Zeydi A","year":"2023","journal-title":"J Nurs Rep Clinic Pract"},{"issue":"1","key":"e_1_2_8_8_1","first-page":"47","article-title":"Implementation of artificial intelligence in the nursing profession: barriers & facilitators","volume":"2","author":"Gholampour MH","year":"2023","journal-title":"J Nurs Rep Clinic Pract"},{"key":"e_1_2_8_9_1","article-title":"Machine learning for assessment of antimicrobial susceptibility: a novel bioinformatics approach against antimicrobial resistance","author":"Hedayati CM","year":"2023","journal-title":"J Nurs Rep Clinic Pract"},{"issue":"1","key":"e_1_2_8_10_1","first-page":"4","article-title":"Applications of machine learning for hemodialysis nursing cares based on a machine learning algorithm","volume":"1","author":"Zabihi MR","year":"2023","journal-title":"J Nurs Rep Clinic Pract"},{"issue":"1","key":"e_1_2_8_11_1","first-page":"3","article-title":"Applications of machine learning for nursing monitoring of electroencephalography","volume":"2","author":"Zabihi MR","year":"2023","journal-title":"J Nurs Rep Clinic Pract"},{"key":"e_1_2_8_12_1","first-page":"1","article-title":"Applications of machine learning for nursing monitoring of electroencephalography","volume":"2","author":"Zabihi MR","year":"2023","journal-title":"J Nurs Rep Clinic Pract"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1089\/wound.2021.0073"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2020.100139"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.burns.2021.07.007"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-020-01542-z"},{"key":"e_1_2_8_17_1","unstructured":"FooteKD.A brief history of machine learning. 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